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Breast Contour Extraction and Pectoral Muscle Segmentation in Digital Mammograms

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					                                                             (IJCSIS) International Journal of Computer Science and Information Security,


        Breast Contour Extraction and Pectoral Muscle
           Segmentation in Digital Mammograms

                    Arun Kumar M.N                                                            H.S. Sheshadri
    Research Scholar, Department of Electronics and                            Department of Electronics and Communication
             Communication Engineering                                                         Engineering
             P.E.S. College of Engneering                                             P.E.S. College of Enginering
                    Mandya, India                                                             Mandya, India
            akmar_mn11@rediffmail.com                                                  hssheshadri@hotmail.com


Abstract— Breast cancer is one of the major causes of fatality           systems are quite high, the false positive detection rates are
among women aged above 40. Digital mammography is used by                also high. Accordingly, work continues on improving all
radiologists for analysis and interpretation of cancer. Visual           aspects    of   computer-aided        detection   (CAD)    for
reading and interpretation of mammograms is a very demanding             mammography. Implementation of breast border detection,
and expensive job. Even well-trained experts may have an
interobserve variation rate of 65-75 percent. Extraction of the
                                                                         because of some factors such as the low contrast near the
breast contour and pectoral muscle segmentation is necessary in          borders, image noise and artifacts is complicated.
order to limit the search for abnormalities by Computer Aided
Diagnosis (CAD). A new technique for breast border extraction                 In mammogram, image processing [27-31] and computer-
and pectoral muscle segmentation is explored in this paper. The          aided diagnosis of breast cancer breast segmentation is an
technique is applied to 250 MIAS mammograms. This method                 important pre-processing step. The accuracy and efficiency of
has given about 98% in segmenting the pectoral muscle.                   processing algorithms will be increased if the processing is
                                                                         limited to a specific target region in an image.
Keywords –Image Processing, mammography, morphology, filter,
edge detection.
                                                                               Extracting the pectoral muscle [23, 24, 25] is particularly
                                                                         important in automated mammogram image assessment.
                     I. INTRODUCTION                                     Segmentation of the pectoral muscle is a non-trivial, complex
                                                                         and demanding task. It is also complicated further by a
     One of the leading causes of death among women is the               number of factors. Foremost thing is, the muscle edge is not a
breast cancer. Early diagnosis and subsequent treatment can              straight line, but can be convex, concave or a mixture of both.
significantly improve the chance of survival for patients with           Secondly muscle edge though may appear to be visually
breast cancer. Most effective method for the detection of early          continuous; the edge exhibits variations in texture and
breast cancer is mammography. Mammograms are among the                   sharpness. This paper describes a new technique for extracting
most difficult radiological images to interpret by radiologists.         the breast border and segmenting the pectoral muscle of digital
Studies have shown that radiologists do not detect all breast            mammograms.
cancers that are retrospectively detected on the mammograms.
Detection is the ability to identify potential abnormalities,                 The remainder of this paper is organized as follows. In
such as microcalcification, masses, and architectural                    Section 2, the approaches to extraction of breast border and
distortions. Diagnosis is the ability to characterize or classify        segmentation of pectoral muscle are described. The theory and
a detected abnormal entity as being either benign or malignant.          proposed techniques are presented in Section 3. Experimental
However, before CADe algorithms can perform their task of                results are given and discussed in Section 4. Finally, the paper
identifying suspicious regions in a mammogram, a series of               is summarized in Section 5.
pre-processing steps must be taken. These include:
mammogram orientation, label and artifact removal,                          II. PREVIOUS APPROACHES TO BREAST BORDER
mammogram enhancement, breast contour detection and                               EXTRACTION AND PECTORAL MUSCLE
pectoral muscle segmentation                                                               SEGMENTATION

     Many computer algorithms [1, 2, 3] have been proposed                    There have been various approaches to the task of
for automating various aspects of detecting the presence of              isolating the breast region.
cancer in mammograms. While detection rates for automatic




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                                                                                                    ISSN 1947-5500
                                                            (IJCSIS) International Journal of Computer Science and Information Security,

      M. Wirth et al. developed an algorithm [1] that uses              [19] are implemented on a number of mammogram images by
morphological preprocessing and fuzzy rule-based algorithm              Ayman et.al. The segmentation outputs of these methods were
for breast region extraction. Kostas Marias et al. [2] used the         very efficient and excellent. Method proposed in [20] applies
boundary extraction technique based on a combination of the             the meta-heuristic methods such as Ant Colony Optimization
Hough transform followed by image gradient operators and                (ACO) and Genetic Algorithm (GA) for identification of
morphology in order to make coherent the breast region part of          suspicious region in mammograms.
the image. Histogram equalization and thresholding process
are employed by Barba J. Leiner et al. [3] to extract only the
                                                                            There have been various approaches to the task of
region of the image that corresponds to the breast.
                                                                        segmenting the pectoral muscle.
Segmentation of the breast region in mammograms has
traditionally been achieved using methods besides active                     A histogram-based thresholding technique is used by K.
contours [4]. Semmlow et al. [5] used a spatial filter and Sobel        Thangavel and M. Karnan [23] to separate the pectoral muscle
edge detector to locate the breast boundary on                          region. For selecting the threshold value the global optimum
xeromammograms. Global thresholding has been used in                    is considered. The intensity values smaller than global
many cases to segment the breast region from the background             optimum threshold are changed to zero, and the gray values
[6-7]. The major problem with using global thresholding is the          greater than the threshold are changed to one. To better
nonuniform background region, although efforts, such as that            preserve the pectoral muscle region erosion and dilation
of Masek et al. [8] using local thresholding have shown more            operations are applied. To segment the pectoral muscle region
promise.                                                                the gray level mammogram image is converted to binary
                                                                        image. The white pixels in the lower left corner of the
     A system of masking images with different thresholds to            mammogram image indicate the pectoral muscle region.
find the breast edge is developed by Abdel-Mottaleb et al. [9].
Gradient based method is proposed by Méndez et al. [10] to                    Kwork et al. [24] developed a method for automatic
find the breast contour. They used a two level thresholding             pectoral muscle segmentation on mammograms by straight
technique to isolate the breast region of the mammogram. The            line estimation and cliff detection. A straight line estimates the
smoothed mammogram is divided into three regions and then               muscle edge and cliff detection refines the detected edge by
a tracking algorithm is applied to the mammogram to detect              surface smoothing and edge detection in a restricted
the border. Bick et al. [11] proposed a global segmentation             neighborhood.
approach that incorporates aspects of thresholding, region
growing and morphological filtering. Lou et al. [12] proposed                H. Mirzaalian et al. developed [25] a new method for the
a method based on the assumption that the trace of intensity            identification of the pectoral muscle in MLO mammograms.
values from the breast region to the air-background is a                The developed method is based on nonlinear diffusion
monotonic decreasing function.                                          algorithm. They compared their results by those recognized by
                                                                        two expert radiologists. To evaluate the accuracy of proposed
     One of the inherent limitations of these methods is the            method, HDM (Hausdorff Distance Measure) and MAEDM
fact that very few of them preserve the skin or nipple. The             (Mean of Absolute Error Distance Measure) were used.
most promising method of extracting the breast contour
focuses on modeling the non-breast region of a mammogram                     R.J. Ferrari proposed [26] a new method for the
using a polynomial method, as described by Chandrasekhar                identification of the pectoral muscle in MLO mammograms
and Attikiouzel [13, 14].                                               based upon a multiresolution technique using Gabor wavelets.
                                                                        This new method overcomes the limitation of the straight-line
     Maysam Shahedi et al. proposed a new algorithm [15] for            representation considered in their initial investigation. The
automatic breast border detection in digital mammograms                 results of the Gabor-filter-based method indicated low
based on local adaptive thresholding method.           Roshan           Hausdorff distances with respect to the hand-drawn pectoral
Dharshana Yapa et.al. presented a new algorithm [16] for                muscle edges.
estimating skin-line and breast segmentation using fast
marching algorithm. They introduced some modifications to                    Mario Mustra et al. [17] uses wavelet decomposition,
the traditional fast marching method, specifically to improve           image blurring and edge detection using the Sobel filter for
the accuracy of skin-line estimation and breast tissue                  breast border detection and pectoral muscle segmentation. N.
segmentation.                                                           Nicolau et al. [34] proposed the use of Independent
                                                                        Component Analysis (ICA) for identification and subsequent
     The method proposed in [17] initially determines                   removal of the pectoral muscle.
intensity value of the background to be able to find pixels that
create the border line. Then breast centre has been taken as             III. PROPOSED BREAST BORDER EXTRACTION AND
the starting point for a simple region growing algorithm. H.              PECTORAL MUSCLE SEGMENTATION TECHNIQUE
Mirzaalian et al. proposed an algorithm [18] based on
polynomial modeling to detect breast contour. Two methods




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                                                                                                    ISSN 1947-5500
                                                                      (IJCSIS) International Journal of Computer Science and Information Security,

    The block diagram for pectoral muscle segmentation is
shown in Fig. 1. Short description of each block is given.




                        Mammogram input
                                                                                       (a)                                                            (b)

                      Breast Border Detection                                  Figure 2: Results for MIAS image mdb003 (a). Original image; (b). Artifacts
                                                                                                        removed in the mdb003

                                                                               Edge Detection and Filtering Techniques
       Locate the Region Containing the Pectoral Muscle
                                                                                    This step uses the Sobel edge detector followed by
                                                                               dithering and 2-D order statistic filtering. The Sobel method
                                                                               finds edges using the Sobel approximation to the derivative.
                      Wavelet Decomposition                                    Edge detection is followed by dithering. A logical OR
                                                                               operation is done on dithered and edge detected image. A 2-D
                                                                               order static filtering is applied on the image obtained as a
                                                                               result of the previous steps. The result for mdb003 is shown in
                                                                               Fig. 3 after applying these steps.

       Mammogram with Pectoral Muscle Segmentation


      Figure 1: Steps carried out for pectoral muscle segmentation.

3.1 Breast Border Detection
                                                                                      (a)                           (b)                        (c)
      We explored a new technique for breast region
segmentation using morphological and filtering techniques.
The steps followed to detect the breast border involves: -                     Figure 3: Results for MIAS image mdb003 (a). Edge detection; (b). Dithering
Removal of noise by median filter, Artifacts removal by                                                 ; (c). 2-D statistic filtering
morphological operation, Edge detection using Sobel method,
filtering, finding the perimeter of the binarized image and thus               Multidimensional image filtering
detect the breast border.
                                                                                     This step removes the noises using a multidimensional
Removal of Noise                                                               image filtering. A rotationally symmetric Gaussian low pass
                                                                               filter filters the image. After that the image is converted to
      Median filter is used to remove the noise. It is the                     binary image and erosion is carried out. Fig. 4 shows the
nonlinear filter used to remove the impulsive noise from an                    results for MIAS image mdb003 after applying these steps.
image. Median filter is a spatial filtering operation. In the
proposed median filter output pixel contains the median value
in the 3X3 neighborhood around the corresponding pixel in
the input image.

Artifacts Removal

     The original mammogram is opened by using a suitable
structuring element. After the opening of mammogram it is                                      Figure 4: Results for MIAS image mdb003
reconstructed. Next step is to threshold the difference image
with 102, which is experimentally obtained. Finally                            Find perimeter pixels in binary image and superimpose on the
morphological operators are applied to smooth irregularities                   original image
and expand region. Fig. 2 shows the results of these steps on
MIAS image mdb003.                                                                 Finally the perimeter pixels in binary image are found.
                                                                               This perimeter is the boundary of the breast image. Fig. 5




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                                                                                                               ISSN 1947-5500
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shows the results. A pixel is the part of the perimeter if it is                Now a line FG is drawn parallel to the line BD through E. It
nonzero and it is connected to at least one zero-valued pixel.                  can be seen that for all the 250 images the reduced rectangle
The connectivity used is 8.                                                     AFGD still include the pectoral muscle. Fig. 8 shows this
                                                                                result for mdb016.




       Figure 5: Contour superimposed on original image mdb003.

3.2 Locate the region containing the pectoral muscle

     Pectoral muscle detection is a challenging task in the
                                                                                  Figure 8: The reduced area that containing the pectoral muscle region is
breast segmentation process. The algorithm for pectoral
                                                                                                           enclosed in AFGD.
muscle segmentation proposed in this paper consists of few
steps. Technique for segmenting pectoral muscle presented in
this paper uses wavelet decomposition, and edge detection
                                                                                3.3 Wavelet decomposition
using the Canny filter.
                                                                                      Wavelet decomposition of fourth level is being done.
      The region of interest containing pectoral muscle is
                                                                                Fourth level wavelet decomposition gives the best results for
determined by two steps. First a rectangle which encloses the
                                                                                detecting larger structures, such as pectoral muscle. The fourth
pectoral muscle is determined and then a refinement/reduction
                                                                                level decomposition gives the best results because it preserves
to this rectangle is done so that the processing time for
                                                                                enough rough details while at the same time remove fine
pectoral muscle segmentation can be still reduced. The initial
                                                                                details like noise and granulation. In this paper, a Daubechies
rectangle is formed by three points A B and C. For example, if
                                                                                filter has been used. Daubechies wavelets are a family of
the image shows MLO view of the right breast, the first point
                                                                                orthogonal wavelets defining a discrete wavelet transform and
A is top left corner of the image with coordinates (1,1). The
                                                                                characterized by a maximal number of vanishing moments for
second point B is determined by the contour of skin-air
                                                                                some given support. With each wavelet type of this class, there
interface. The third point C is chosen to be approximately at
                                                                                is a scaling function which generates an orthogonal
half of image height. By those three points a rectangle is
                                                                                multiresolution analysis. Fig 9 shows a Daubechies 20 2-d
determined. Fig. 7 shows the breast contour superimposed on
                                                                                wavelet.
the image mdb016 and the rectangle ABCD determined.




   Figure 7: Breast contour superimposed on the image mdb016 and the
                      rectangle ABCD determined.
                                                                                                   Figure 9 : Daubechies 20 2-d wavelet

     The reason to reduce the size of the rectangle is to reduce
                                                                                      After the wavelet decomposition edges that were detected
the processing time for pectoral muscle segmentation and is
                                                                                by the Canny filter inside the pectoral muscle region are
done in the following way. A new point E is determined on the
                                                                                removed by approximating muscle boundary with a straight
breast contour in such a way that point E on the breast contour
                                                                                line that connects upper right corner and lower left corner of
has a maximum distance from the line BD towards point A.
                                                                                muscle region in the case of the right breast image.




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                                                                                       Some of the results of the proposed method for pectoral
                                                                                  muscle identification is explained below. Fig. 12 shows the
                IV. EXPERIMENTAL RESULTS                                          successful results of the proposed method.

     The proposed method applied to 250 mammograms from
Mammography Image Analysis Society (MIAS) database [21].
The various results obtained are discussed below. Evaluation
of breast contour detected in the mammograms was performed
by the Hausdorff Distance Measure (HDM) [22] and also the
Mean of Absolute Error Distance Measure (MAEDM).
Evaluation is based on a distance transforms and image
algebra between the edges identified by radiologists and by
proposed method. The accuracy of contour detection is 99.06.
                                                                                        (a)                               (b)                          (c)
      Some of the results of the proposed method for breast
contour extraction are explained below. Fig. 10 shows the
successful results of the proposed method. Fig. 11 shows the
failure case.




                                                                                           (d)                           (e)

                                                                                   Figure 12: Pectoral muscle identification results for MIAS image mdb016.
                                                                                  (a).Breast contour superimposed on original image; (b). The region of interest
                                                                                  that contain the pectoral muscle; (c). Segmented area that contain the pectoral
          (a)                      (b)                             (c)            muscle; (d). Wavelet decomposed image; (e). Pectoral muscle edge identified
                                                                                                                     on image.



                                                                                                            V. CONCLUSION.

                                                                                       In this paper a method for the detection of the breast
                                                                                  contour and pectoral muscle segmentation is presented. The
       (d)                                                                        proposed method for detecting the breast border contour is
Figure 10: Mammogram segmentation results for MIAS image mdb016. (a).             tested on the 250 MIAS datasets. This method gave 99.06
  Original Mammogram; (b). Noise & Artifacts removal after filtering and          successes in detecting the correct skin-air interface. The
 morphological operation. (c). Binary Image; (d). Contour superimposed on
                                                                                  proposed method fails in detecting the correct skin-air
                                 original.
                                                                                  interface for very few mammograms because of the noise (big
                                                                                  size artifacts). Advantage of this method is low algorithm
                                                                                  complexity and therefore short processing time. Our further
                                                                                  development concerns smoothing of the breast border and
                                                                                  pectoral muscle segmentation line. The proposed technique is
                                                                                  fully autonomous, and is able to preserve the skin and nipple.

                                                                                       Pectoral muscle detection is a challenging task because it
                                                                                  is not very well differenced from the surrounding breast tissue.
                                                                                  There is different intensity variation of the pectoral muscle
                                                                                  and the surrounding tissue for each mammogram images. The
          (a)                   (b)                          (c)                  method proposed in this paper uses wavelet decomposition.
                                                                                  This approach works well with an accuracy of 98% because
Figure 11: Mammogram segmentation results for MIAS mdb012. (a). Original          pectoral muscle is rather large object for detection. Future
      Mammogram; (b). Image after removal of artifacts; (c) Contour               work will focus on smoothening the breast contour and
                   superimposed on original image.                                pectoral muscle edge.




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Services(WIAMIS'07)




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[31] N. Nicolaou, S. Petroudi, J. Georgiou, M. Polycarpou, and M. Brady,
“Digital Mammography: Towards Pectoral Muscle Removal via Independent
Component Anlysis”, Department of Electrical and Computer Engineering,
                                                                                Dr. H.S. Sheshadri is working as a Professor in the
University of Cyprus, 1678 Nicosia, CyprusFax. And Wolfson Medical              Department of Electronics & Communication Engineering,
Vision Laboratory, Oxford University, Oxford OX2 7DD, UK.                       PES College of Engineering Mandya, Karnataka. He received
                                                                                his B.E from University of Mysore in 1980 and Ph.D from
                     AUTHORS PROFILE                                            PSG Institute of Technology , Coimbatore, Tamilnadu, India.
Arun kumar M.N is a research scholar in PES college of                          He has published many research papers in International
Engineering, Mandya, Karnataka, India. He graduated from                        Journals. His research area includes Image Processing, and
Mysore University in Computer Science and Engineering in                        Computer Vision.
1996. He received his M.Sc(Engg.) from Visvesvaraya
Technological University, Belgaum, Karnataka. His research
interest includes Data Mining, and Image Processing.




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                                                                                                         ISSN 1947-5500

				
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Description: The International Journal of Computer Science and Information Security (IJCSIS Vol. 9 No. 2) is a reputable venue for publishing novel ideas, state-of-the-art research results and fundamental advances in all aspects of computer science and information & communication security. IJCSIS is a peer reviewed international journal with a key objective to provide the academic and industrial community a medium for presenting original research and applications related to Computer Science and Information Security. . The core vision of IJCSIS is to disseminate new knowledge and technology for the benefit of everyone ranging from the academic and professional research communities to industry practitioners in a range of topics in computer science & engineering in general and information & communication security, mobile & wireless networking, and wireless communication systems. It also provides a venue for high-calibre researchers, PhD students and professionals to submit on-going research and developments in these areas. . IJCSIS invites authors to submit their original and unpublished work that communicates current research on information assurance and security regarding both the theoretical and methodological aspects, as well as various applications in solving real world information security problems.